“You need to measure what’s happening in your community. If you are interested in knowing what your community members are up to, what information they are sharing and looking at, what they are saying about you, your product or your service (positive and negative), then you need social analytics.

If you need to know how many users are signing up, how many are contributing to blogs, wikis, forums, how many are asking and answering questions, then you need social analytics.

With social computing becoming much more mainstream and in many cases, a requirement for both external and internal relationship building, it has become critical to measure the impact these solutions really have. You also need to know how and where to improve these solutions.

And you aren’t going to get this information from traditional web traffic analytics.”

Natalie L. Petouhoff, an analyst at Forrester Research, said that online user groups conform to what she calls the 1-9-90 rule. About 1 percent of those in the community, she explained, are super-users who supply most of the best answers and commentary. An additional 9 percent are “responders” who mainly reply and rate Web posts, she said, and the other 90 percent are “readers” who primarily peruse and search the Web site for useful information.

I would extend this point and add: within the 1% of active users are all the different types of active contribution, both good and bad. Top answer people, discussion starters, discussion people, question people, and flame warriors all crowd into this sliver of the online demographic. It is important to have the tools to separate the different kinds of active contributions to be sure that an active community is also a properly productive one!

which suggests that just publishing the unique pattern of links around an individual is sufficient to identify them in an otherwise anonymized data base.

Abstract:
Operators of online social networks are increasingly sharing
potentially sensitive information about users and their relationships
with advertisers, application developers, and data-mining researchers.
Privacy is typically protected by anonymization, i.e., removing names,
addresses, etc.
We present a framework for analyzing privacy and anonymity in social
networks and develop a new re-identification algorithm targeting
anonymized social-network graphs. To demonstrate its effectiveness on
real-world networks, we show that a third of the users who can be
verified to have accounts on both Twitter, a popular microblogging
service, and Flickr, an online photo-sharing site, can be re-identified
in the anonymous Twitter graph with only a 12% error rate.
Our de-anonymization algorithm is based
purely on the network topology, does not require creation of a large
number of dummy “sybil” nodes, is robust to noise and all existing
defenses, and works even when the overlap between the target network
and the adversary’s auxiliary information is small.